Leveraging Machine Learning Models for Mental Health Data Exploration

Early prediction of mental health disorders is now a global challenge, aiming at detecting it at early stages and providing the required support as fast as possible. Millions can be affected by it, whether with complete knowledge of their case or not; it requires very professional assistance and healthcare resources to identify how to deal with affected individuals. Machine learning models can help identify individuals at risk of mental health disorders and assess the severity of their condition. Data can be integrated from multiple sources, such as wearable devices, social media, clinical records, or questionnaires. The process can include handling different data types, feature engineering, hyper-tuning of parameters, and interoperability of results. Utilizing different models such as baseline models, tree-based models, and neural networks in predicting the required outcomes. The study provides a comparative analysis of different machine learning models and their accuracy of predictions. This paper aims to contribute to the research area of mental health disorders prediction through a machine learning and data-driven approach.